ATAC-Seq/ RNA-Seq (PND15 vs Adults): Diff accessible peaks and diff exp genes
Library
Data
CPM
ATAC
TMM
Loess
loess_atac <- log1p(assay(atac_loess, i = 1)) - assay(atac_loess, i = 2)
colnames(loess_atac) <- gsub(pattern = ".*/|\\.bam", replacement = "", x = colData(atac_loess)[, 1])
rownames(loess_atac) <- rowData(atac_loess)[, 6]
loess_atac <- data.frame(Peak = rownames(loess_atac), Genes = rowData(atac_loess)[, "Peak-SYMBOL"], loess_atac)Peaks
dar_tmm <- data.frame(rowData(atac_tmm), stringsAsFactors = F, check.names = F)
peaks_tmm <- dar_tmm[abs(dar_tmm$`diffAccessibility-logFC`) >= 1 &
dar_tmm$`diffAccessibility-qvalue` <= 0.05, 6]
dar_loess <- data.frame(rowData(atac_loess), stringsAsFactors = F, check.names = F)
peaks_loess <- dar_loess[abs(dar_loess$`diffAccessibility-logFC`) >= 1 &
dar_loess$`diffAccessibility-qvalue` <= 0.05, 6]
peaks_common <- intersect(peaks_tmm, peaks_loess)
peaks_diff <- setdiff(peaks_tmm, peaks_loess)Expression
Merging Expression with ATAC
TMM
ca <- data.frame(distanceTSS = rowData(atac_tmm)[, "Peak-distanceToTSS"], cpm_atac)
ca <- ca[abs(ca$distanceTSS) <= 5000, ]
cpm_atac_rna <- merge(ca[, -1], rna_cpm_df)
rownames(cpm_atac_rna) <- cpm_atac_rna$Peak
cpm_rna <- cpm_atac_rna[, grep(pattern = "RS_", x = colnames(cpm_atac_rna), invert = T)]
rna_cpm <- cpm_atac_rna[, grep(pattern = "Genes|Peak|RS_", x = colnames(cpm_atac_rna))]
colnames(rna_cpm) <- gsub(pattern = "RS_", replacement = "", x = colnames(rna_cpm))Loess
la <- data.frame(distanceTSS = rowData(atac_loess)[, "Peak-distanceToTSS"], loess_atac)
la <- la[abs(la$distanceTSS) <= 5000, ]
loess_atac_rna <- merge(la[, -1], rna_cpm_df)
rownames(loess_atac_rna) <- loess_atac_rna$Peak
loess_rna <- loess_atac_rna[, grep(pattern = "RS_", x = colnames(loess_atac_rna), invert = T)]
rna_loess <- loess_atac_rna[, grep(pattern = "Genes|Peak|RS_", x = colnames(loess_atac_rna))]
colnames(rna_loess) <- gsub(pattern = "RS_", replacement = "", x = colnames(rna_loess))Peaks overlapping
peaks_tmm_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_tmm]
peaks_loess_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_loess]
peaks_common_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_common]
peaks_sd_rna <- rownames(cpm_atac_rna)[rownames(cpm_atac_rna) %in% peaks_diff]Peaks table
df_peaks <- data.frame(
Group = c("TMM", "Loess", "Common", "setDiff"),
n = c(
length(peaks_tmm), length(peaks_loess),
length(peaks_common), length(peaks_diff)
),
overlapRNA_dissTSS_5k = c(
length(peaks_tmm_rna), length(peaks_loess_rna),
length(peaks_common_rna), length(peaks_sd_rna)
)
)
knitr::kable(x = df_peaks, format = "html", align = "c", row.names = FALSE)| Group | n | overlapRNA_dissTSS_5k |
|---|---|---|
| TMM | 3212 | 654 |
| Loess | 2901 | 590 |
| Common | 2580 | 529 |
| setDiff | 632 | 125 |
Heatmaps
anno_atac <- data.frame(Group = colnames(cpm_atac)[-c(1:2)])
rownames(anno_atac) <- anno_atac$Group
anno_atac$Group <- gsub(pattern = "_.*", replacement = "", x = anno_atac$Group)
an_atac <- HeatmapAnnotation(
Groups = anno_atac$Group,
col = list(Groups = c("PND15" = "darkgreen", "Adult" = "Maroon"))
)
anno_rna <- rna_tmm$pData[, 1, drop = FALSE]
anno_rna <- anno_rna[grep(pattern = "PND8", x = rownames(anno_rna), invert = T), , drop = FALSE]
an_rna <- HeatmapAnnotation(
Groups = anno_rna$Group,
col = list(Groups = c("PND15" = "darkgreen", "Adult" = "Maroon"))
)TMM peaks
TMM + Loess
tmm_peak_1 <- Heatmap(
matrix = t(scale(t(cpm_atac[peaks_tmm, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
tmm_peak_2 <- Heatmap(
matrix = t(scale(t(loess_atac[peaks_tmm, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(tmm_peak_1 + tmm_peak_2)
RNA + TMM + Loess
tmm_peak_3 <- Heatmap(
matrix = t(scale(t(rna_cpm[peaks_tmm_rna, -c(1:2)]))),
column_title = "Gene expression", name = "RS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_rna
)
tmm_peak_4 <- Heatmap(
matrix = t(scale(t(cpm_rna[peaks_tmm_rna, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
tmm_peak_5 <- Heatmap(
matrix = t(scale(t(loess_rna[peaks_tmm_rna, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(tmm_peak_3 + tmm_peak_4 + tmm_peak_5)
Loess peaks
TMM + Loess
loess_peak_1 <- Heatmap(
matrix = t(scale(t(cpm_atac[peaks_loess, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
loess_peak_2 <- Heatmap(
matrix = t(scale(t(loess_atac[peaks_loess, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(loess_peak_1 + loess_peak_2)
RNA + TMM + Loess
loess_peak_3 <- Heatmap(
matrix = t(scale(t(rna_loess[peaks_loess_rna, -c(1:2)]))),
column_title = "Gene expression", name = "RS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_rna
)
loess_peak_4 <- Heatmap(
matrix = t(scale(t(cpm_rna[peaks_loess_rna, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
loess_peak_5 <- Heatmap(
matrix = t(scale(t(loess_rna[peaks_loess_rna, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(loess_peak_3 + loess_peak_4 + loess_peak_5)
Common peaks
TMM + Loess
c_peak_1 <- Heatmap(
matrix = t(scale(t(cpm_atac[peaks_common, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
c_peak_2 <- Heatmap(
matrix = t(scale(t(loess_atac[peaks_common, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(c_peak_1 + c_peak_2)
RNA + TMM + Loess
c_peak_3 <- Heatmap(
matrix = t(scale(t(rna_cpm[peaks_common_rna, -c(1:2)]))),
column_title = "Gene expression", name = "RS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_rna
)
c_peak_4 <- Heatmap(
matrix = t(scale(t(cpm_rna[peaks_common_rna, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
c_peak_5 <- Heatmap(
matrix = t(scale(t(loess_rna[peaks_common_rna, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(c_peak_3 + c_peak_4 + c_peak_5)
setDiff peaks
TMM + Loess
sd_peak_1 <- Heatmap(
matrix = t(scale(t(cpm_atac[peaks_diff, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
sd_peak_2 <- Heatmap(
matrix = t(scale(t(loess_atac[peaks_diff, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(8, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(sd_peak_1 + sd_peak_2)
RNA + TMM + Loess
sd_peak_3 <- Heatmap(
matrix = t(scale(t(rna_cpm[peaks_sd_rna, -c(1:2)]))),
column_title = "Gene expression", name = "RS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_rna
)
sd_peak_4 <- Heatmap(
matrix = t(scale(t(cpm_rna[peaks_sd_rna, -c(1:2)]))),
column_title = "TMM Normalization", name = "AS: TMM",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
sd_peak_5 <- Heatmap(
matrix = t(scale(t(loess_rna[peaks_sd_rna, -c(1:2)]))),
column_title = "Loess Normalization", name = "AS: Loess",
show_row_names = FALSE, cluster_columns = FALSE,
na_col = "grey", km = 2, show_column_names = FALSE,
width = unit(6, "cm"), height = unit(12, "cm"),
top_annotation = an_atac
)
draw(sd_peak_3 + sd_peak_4 + sd_peak_5)
Plots of logFC values
Date
dar_tmm_s <- dar_tmm[,grep(pattern = "Name|diff", x = colnames(dar_tmm))]
colnames(dar_tmm_s) <- gsub(pattern = "diffAccessibility", replacement = "TMM", x = colnames(dar_tmm_s))
dar_loess_s <- dar_loess[,grep(pattern = "Name|diff", x = colnames(dar_loess))]
colnames(dar_loess_s) <- gsub(pattern = "diffAccessibility", replacement = "Loess", x = colnames(dar_loess_s))
dar_tab <- merge(dar_tmm_s, dar_loess_s)
rownames(dar_tab) <- dar_tab$`Peak-Name`Function to make plot
logFC_plot <- function(res){
ggplot(data = res, aes(x = `TMM-logFC`, y = `Loess-logFC`)) +
geom_point(alpha = 0.2, size = 1) +
ggtitle("Log2 Fold Change") +
labs(subtitle="lm fit") +
xlab("TMM normalization") +
ylab("Loess normalization") +
theme(
plot.title = element_text(face = "bold", size = 20, hjust = 0.5),
plot.subtitle=element_text(size=16, hjust=0.5, face="italic", color="blue"),
axis.title.x = element_text(face = "bold", size = 15),
axis.text.x = element_text(face = "bold", size = 12),
axis.title.y = element_text(face = "bold", size = 15),
axis.text.y = element_text(face = "bold", size = 12),
legend.title = element_text(face = "bold", size = 15),
legend.text = element_text(size = 14)
) +
stat_smooth(se = FALSE, method = "lm", color = "red", formula = y ~ x, size = 0.5)
}
SessionInfo
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.2 (2019-12-12)
## os Ubuntu 16.04.6 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Europe/Zurich
## date 2020-05-20
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## AnnotationDbi 1.48.0 2019-10-29 [1]
## askpass 1.1 2019-01-13 [1]
## assertthat 0.2.1 2019-03-21 [1]
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## DBI 1.1.0 2019-12-15 [1]
## dbplyr 1.4.3 2020-04-19 [1]
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## GenomeInfoDb * 1.22.1 2020-03-27 [1]
## GenomeInfoDbData 1.2.2 2019-11-18 [1]
## GenomicAlignments 1.22.1 2019-11-12 [1]
## GenomicFeatures 1.38.2 2020-02-15 [1]
## GenomicRanges * 1.38.0 2019-10-29 [1]
## GetoptLong 0.1.8 2020-01-08 [1]
## ggplot2 * 3.3.0 2020-03-05 [1]
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## IRanges * 2.20.2 2020-01-13 [1]
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## labeling 0.3 2014-08-23 [1]
## lattice 0.20-41 2020-04-02 [1]
## lazyeval 0.2.2 2019-03-15 [1]
## lifecycle 0.2.0 2020-03-06 [1]
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## shape 1.4.4 2018-02-07 [1]
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## source
## Bioconductor
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## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.1)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.1)
## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.1)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.2)
## Bioconductor
##
## [1] /home/ubuntu/R/x86_64-pc-linux-gnu-library/3.6
## [2] /usr/local/lib/R/site-library
## [3] /usr/lib/R/site-library
## [4] /usr/lib/R/library